https://doi.org/10.1140/epjc/s10052-023-11277-5
Regular Article - Theoretical Physics
Holographic dark energy through Kaniadakis entropy in non flat universe
1
Department of Mathematics, Institute of Applied Sciences and Humanities, GLA University, 281406, Mathura, Uttar Pradesh, India
2
IT Department (Math Section), University of Technology and Applied Sciences-Salalah, Salalah, Oman
3
Department of Mathematics (BSH), Pranveer Singh Institute of Technology, 209305, Kanpur, Uttar Pradesh, India
c
sharma.umesh@gla.ac.in
d
pankaj.fellow@yahoo.co.in
Received:
10
January
2023
Accepted:
29
January
2023
Published online:
14
February
2023
By extending the standard holographic principle to a cosmological framework and combining the non-flat condition with the Kaniadakis entropy, we construct the non-flat Kaniadakis holographic dark energy (KHDE) model. The model employs Kaniadakis parameter K and a parameter c. Derivation of the differential equation for KHDE density parameter to describe the evolutionary behavior of the universe is obtained. Such a differential equation could explain both the open as well as closed universe models. The classification based on matter and dark energy (DE) dominated regimes show that the KHDE scenario may be used to specify the universe’s thermal history and that a quintom regime can be encountered. For both open and closed, we find the expressions for the deceleration parameter and the equation of state (EoS) parameter. Also, by varying the associated parameters, classical stability of the method is established. On considering the curvature to be positive, the universe favors the quintom behavior for substantially smaller values as opposed to the flat condition, when only quintessence is attained for such K values. Additionally, we see a similar behavior while considering the negative curvature for such K values. Therefore, adding a little bit of spatial geometry that isn’t flat to the KHDE enhances the phenomenology while maintaining K values at lower levels. To validate the model parameters, the most recent 
dataset, in the redshift range 
are utilized. In addition, the distance modulus from the current Union 2.1 data set of type SNIa are employed.
© The Author(s) 2023
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